Nitrogen-containing compounds, including glycine betaine (GBT), choline and trimethylamine N-oxide (TMAO) are ubiquitous in marine organisms. They are used by marine organisms as compatible solutes in response to changes in environmental conditions, such as increasing salinity, because they do not interfere with cell metabolism. They also have beneficial effects in protecting proteins against denaturation due to chemical or physical damage. In the marine environment, these compounds are frequently released from these organisms directly into seawater due to changing environmental conditions, such as by viral lysis or grazing. The released nitrogenous osmolytes serve as important nutrients for marine microorganisms, which can use them as carbon, nitrogen and energy sources. It is well known that the degradation of these nitrogenous osmolytes contribute to the release of climate-active gases, including volatile methylamines. Methylamines are important sources of aerosols in the marine atmosphere, which help to reflect sunlight and cause a cooling effect on the climate. Our NERC-funded research is starting to understand the microbial metabolism of these compounds and their seasonal cycles in the coastal surface seawater, but our understanding across the world's oceans is limited. Of particular importance to the Earth's climate is the Southern Ocean. The Southern Ocean is an important player in the Earth climate system, and is an ideal region to study ocean-atmosphere connections because of its isolation from continental emissions and the strong circumpolar atmospheric circulation, rendering its air pristine. Opportunities to study the Southern Ocean are rare however, and it remains under sampled even for the most routine measurements compared to the rest of the World's oceans. We have a unique opportunity within the Antarctic Circumnavigation Expedition (ACE) to make measurements and collect samples around the entire Southern Ocean, and near Antarctica. Twenty one other international projects will also be conducting research from the same expedition, and six of these projects have excellent links to our research. Unfortunately, there are no plans for after the expedition for the projects to collaborate and integrate data, which is a real missed opportunity. This proposal aims to develop a new international network with six ACE projects and use post-cruise activities to exploit data and knowledge generated to capitalise on our NERC-funded research on nitrogenous osmolytes and to increase its international breadth.

A landscape model consists of a parameterised family of potential functions together with a Riemannian metric. The dynamical system associated with this is given by the corresponding gradient vectorfield. Any Morse-Smale dynamical system with only rest point attractors and any system that admits a filtration admits such a representation except in a small neighbourhood of attractors and repellers. Such landscape models are of great interest in Developmental Biology because they correspond to Waddington's famous epigenetic landscapes but can also be rigorously associated with network models of the relevant genetic systems. When used to model the dynamics of a cell the parameters of the landscape correspond to signals being received by the cell. These can be due to morphogens in the cell's environment or signals coming from other cells. When these signal are altered, the landscape changes and this can cause bifurcations which destroy the attractor governing a cell's state and this can lead to a change in the cell's state. This is cellular differentiation, the way by which cell can change their cell type and specification. For example, stem cells differentiate in this way eventually to provide cells for all the tissue types in the body. The formation of the vertebrate trunk provides an important example of how cell fate decisions in developing tissues are made by signal controlled gene regulatory networks. Our biological collaborators have been studying part of this, namely the time course of differentiation of mouse embryonic stem cells to anterior neural or neural-mesodermal progenitors using such multidimensional single cell data. These experiments and the associated mathematical analysis has suggested that underlying this system is a highly non-trivial landscape of a complexity significantly greater than any published. This will be a key exploratory system that we will use to develop our ideas and we will work closely with the Briscoe and Warmflash labs to do this. However, it is important to stress that the purpose of this proposal is to focus strongly on developing mathematical ideas and tools and not just to be embedded in a particular biological project. On the other hand, access to state-of-the art data is very important. It ensures biological relevance and work with real data, rather than simulated data, raises real mathematical challenges. More and more powerful biological tools are becoming available to study such processes but the increasing amount and complexity of the data produced and the fact that the processes are carried out by complex systems means that new mathematical tools are need to help understand what is going on. In particular, biologists can now measure the numbers of multiple molecules in each of tens of thousands of cells in a single experiment. The key aim of this project is to increase our understanding of landscape models and combine this with state-of-the-art statistical techniques to provide new tools to analyse such data and to use it to probe the mechanisms of cellular differentiation and cellular decision-making in some important biological systems. The project involves deep collaboration with biological labs both in terms of data and biological ideas. It will be an excellent example of data science since it involves informatics (bioinformatics), statistics, mathematics (analysis, geometry & probability), hp computing and science (biology). It provides a new method of date dimension reduction a key theme in data science.

The Oxford-Warwick Statistics Programme will train a new cohort of at least 50 graduates in the theory, methods and applications of Statistical Science for 21st Century data-intensive environments and large-scale models. This is joint project lead by the Statistics Departments of Oxford and Warwick. These two departments, ranked first and second for world leading research in the last UK research assessment exercise, can provide a wonderful stimulating training environment for doctoral students in statistics. The Centre's pool of supervisors are known for significant international research contributions in modern computational statistics and related fields, contributions recognised by over 20 major National and International Awards since 2008. Oxford and Warwick attract students with competitively won international scholarships. The programme leaders expect to expand the cohort to 11 or 12 per year by bringing these students into the CDT, and raising their funding up to CDT-level using £188K in support from industry and £150K support from donors. The need to engage in large-scale highly structured statistical models has been recognized for some time within areas like genomics and brain-imaging technologies. However, the UK's leading industries and sciences are now also increasingly aware of the enormous potential that data-driven analysis holds. These industries include the engineering, manufacturing, pharmaceutical, financial, e-commerce, life-science and entertainment sectors. The analysis bottleneck has moved from being able to collect and record relevant data to being able to interpret and exploit vast data collections. These and other businesses are critically dependent on the availability of future leaders in Statistics, able to design and develop statistical approaches that are scalable to massive data. The UK can take a world lead in this field, being a recognized international leader in Statistics; and OxWaSP is ideally placed to realize the potential of this opportunity. The Centre is focused on a new type of training for a new type of graduate statistician in statistical methodology and computation that is scalable to big data. We will bring a new focus on training for research, by teaching directly from the scientific literature. Students will be thrown straight into reading and summarizing journal papers. Lecture-format contact is used sparingly with peer-to-peer learning central to the training approach. This is teaching and learning for research by doing research. Cohort learning will be enhanced via group visits to companies, small groups reproducing results from key papers, student-orientated paper discussions, annual workshops and a three-day off-site retreat. From the second year the students will join their chosen supervisors in Warwick and Oxford, five in each Centre coming together regularly for research group meetings that overlap Oxford and Warwick, for workshops and retreats, and teaching and mentoring of students in earlier years. The Centre is timely and ambitious, designed to attract and nurture the brightest graduate statisticians, broadening their skills to meet the new challenge and allowing them to flourish in a focused, communal, research-training environment. The strategic vision is to train the next generation of statisticians who will enable the new data-intensive sciences and industries. The Centre will offer a vehicle to bring together industrial partners from across the two departments to share ideas and provide an important perspective to our students on the research challenges and opportunities within commercial and social enterprises. Student's training will be considerably enhanced through the Centre's visits, lectures, internships and co-supervision from global partners including Amazon, Google, GlaxoSmithKline, MAN and Novartis, as well as smaller entrepreneurial start-ups Deepmind and Optimor.

With the loss of global biodiversity, signals of the speciation process are progressively being erased as populations dwindle and species disappear. We propose to develop and implement an integrative approach that will draw from new advances in computational biology, field ecology, and population genomics. We anticipate that our approach will be broadly applicable to other non-model biological systems, especially those that diverged recently and rapidly and for which species boundaries are cryptic. Our findings, as well as in-country collaboration, will have immediate relevance to conservation policy in the ecologically complex and poorly studied regions of Southeastern Madagascar.

The combination of computer simulation with experiment is fundamental to achieving new understanding in chemistry, and to delivering advances that can address the most pressing societal challenges. The integration of computer simulation into research across the chemical sciences has been accelerated by the accessibility of high-performance computing infrastructure and tailored software that can harness the distributed architectures. New materials and chemical processes can be predicted by models of atoms and electrons using this infrastructure, with periodic density functional theory (DFT) at the forefront of the field of applied materials simulation. However, the efficacy of these modelling paradigms is proportional to the degrees of freedom in the system, which means that big models with lots of electrons, such as when considering catalytic processes, become very expensive to simulate. To address these shortcomings, this Fellowship looks to improve the capability and accessibility of methods that can provide high-level accuracy for electronic structure simulations, necessary for bond-breaking or bond-forming reactions, with reduced degrees of freedom, which means simulations can be performed quicker. This Fellowship is delivering new multiscale modelling paradigms, and the aim of this renewal is to make these paradigms more accessible through easier to use frameworks, and to extend our capabilities by integrating new machine-learning models into the simulation workflow, with the potential for acceleration in accurately resolving aspects of the system wavefunction. The new capabilities will continue to be developed in internationally leading software packages (FHI-aims, ChemShell) with collaborative partners distributed globally in academia and government research laboratories. The Fellowship will simultaneously look to demonstrate the potential of these new methods, with aims to resolve key mechanistic aspects of the synthesis of renewable fuel in collaboration with experimental partners in academia, notably at the host institution (Cardiff Catalysis Institute, Cardiff University) and via collaborations through the UK Catalysis Hub, as well as industry (Johnson Matthey, bp). The Fellowship aims to provide new knowledge of how the catalytic active site structure defines reactivity and selectivity in processes relating to photo- and electro-catalytic H2 generation; and also to explore how the structure of support materials influences thermally driven catalytic transformation of waste to sustainable aviation fuel. Finally, the Fellowship has complementary aims to support the transition of the research team from emergent researchers to influential and authoritative research leaders who can support the development of both new research domains and the next generation of researchers. The research team will be supported in developing, practising, and reflecting on their leadership activities, so they can deliver lasting impact in their sphere of influence.